KMeans Clustering Analysis

Authors
Affiliation

Ling Lu

Boston University

Luoyan Zhang

Boston University

Yinuo Wang

Boston University

Published

April 21, 2025

Load the Dataset

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
pd.set_option('display.max_columns', None)
df = pd.read_csv("lightcast_job_postings.csv")

df.info()
df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 72498 entries, 0 to 72497
Columns: 118 entries, id to naics_2022_6_name
dtypes: float64(38), object(80)
memory usage: 65.3+ MB
id last_updated_date last_updated_timestamp duplicates posted expired duration title_raw body modeled_expired modeled_duration company company_name company_raw company_is_staffing education_levels education_levels_name min_edu_levels min_edu_levels_name max_edu_levels max_edu_levels_name employment_type employment_type_name min_years_experience max_years_experience is_internship salary remote_type remote_type_name original_pay_period salary_to salary_from location city city_name county county_name msa msa_name state state_name county_outgoing county_name_outgoing county_incoming county_name_incoming msa_outgoing msa_name_outgoing msa_incoming msa_name_incoming naics2 naics2_name naics3 naics3_name naics4 naics4_name naics5 naics5_name naics6 naics6_name title title_name title_clean certifications certifications_name onet onet_name onet_2019 onet_2019_name cip6 cip6_name cip4 cip4_name cip2 cip2_name soc_2021_2 soc_2021_2_name soc_2021_3 soc_2021_3_name soc_2021_4 soc_2021_4_name soc_2021_5 soc_2021_5_name lot_career_area lot_career_area_name lot_occupation lot_occupation_name lot_specialized_occupation lot_specialized_occupation_name lot_occupation_group lot_occupation_group_name lot_v6_specialized_occupation lot_v6_specialized_occupation_name lot_v6_occupation lot_v6_occupation_name lot_v6_occupation_group lot_v6_occupation_group_name lot_v6_career_area lot_v6_career_area_name soc_2 soc_2_name soc_3 soc_3_name soc_4 soc_4_name soc_5 soc_5_name lightcast_sectors lightcast_sectors_name naics_2022_2 naics_2022_2_name naics_2022_3 naics_2022_3_name naics_2022_4 naics_2022_4_name naics_2022_5 naics_2022_5_name naics_2022_6 naics_2022_6_name
0 1f57d95acf4dc67ed2819eb12f049f6a5c11782c 9/6/2024 32:57.4 0.0 6/2/2024 6/8/2024 6.0 Enterprise Analyst (II-III) 31-May-2024\n\nEnterprise Analyst (II-III)\n\n... False 6.0 894731.0 Murphy USA Murphy USA False [\n 2\n] [\n "Bachelor's degree"\n] 2.0 Bachelor's degree NaN NaN 1.0 Full-time (> 32 hours) 2.0 2.0 False NaN 0.0 [None] NaN NaN NaN {\n "lat": 33.20763,\n "lon": -92.6662674\n} RWwgRG9yYWRvLCBBUg== El Dorado, AR 5139.0 Union, AR 20980.0 El Dorado, AR 5.0 Arkansas 5139.0 Union, AR 5139.0 Union, AR 20980.0 El Dorado, AR 20980.0 El Dorado, AR 44.0 Retail Trade 441.0 Motor Vehicle and Parts Dealers 4413.0 Automotive Parts, Accessories, and Tire Retailers 44133.0 Automotive Parts and Accessories Retailers 441330.0 Automotive Parts and Accessories Retailers ET29C073C03D1F86B4 Enterprise Analysts enterprise analyst ii iii [] [] 15-2051.01 Business Intelligence Analysts 15-2051.01 Business Intelligence Analysts [\n "45.0601",\n "27.0101"\n] [\n "Economics, General",\n "Mathematics, Ge... [\n "45.06",\n "27.01"\n] [\n "Economics",\n "Mathematics"\n] [\n "45",\n "27"\n] [\n "Social Sciences",\n "Mathematics and St... 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists 23.0 Information Technology and Computer Science 231010.0 Business Intelligence Analyst 23101011.0 General ERP Analyst / Consultant 2310.0 Business Intelligence 23101011.0 General ERP Analyst / Consultant 231010.0 Business Intelligence Analyst 2310.0 Business Intelligence 23.0 Information Technology and Computer Science 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists [\n 7\n] [\n "Artificial Intelligence"\n] 44.0 Retail Trade 441.0 Motor Vehicle and Parts Dealers 4413.0 Automotive Parts, Accessories, and Tire Retailers 44133.0 Automotive Parts and Accessories Retailers 441330.0 Automotive Parts and Accessories Retailers
1 0cb072af26757b6c4ea9464472a50a443af681ac 8/2/2024 08:58.8 0.0 6/2/2024 8/1/2024 NaN Oracle Consultant - Reports (3592) Oracle Consultant - Reports (3592)\n\nat SMX i... False NaN 133098.0 Smx Corporation Limited SMX True [\n 99\n] [\n "No Education Listed"\n] 99.0 No Education Listed NaN NaN 1.0 Full-time (> 32 hours) 3.0 3.0 False NaN 1.0 Remote NaN NaN NaN {\n "lat": 44.3106241,\n "lon": -69.7794897\n} QXVndXN0YSwgTUU= Augusta, ME 23011.0 Kennebec, ME 12300.0 Augusta-Waterville, ME 23.0 Maine 23011.0 Kennebec, ME 23011.0 Kennebec, ME 12300.0 Augusta-Waterville, ME 12300.0 Augusta-Waterville, ME 56.0 Administrative and Support and Waste Managemen... 561.0 Administrative and Support Services 5613.0 Employment Services 56132.0 Temporary Help Services 561320.0 Temporary Help Services ET21DDA63780A7DC09 Oracle Consultants oracle consultant reports [] [] 15-2051.01 Business Intelligence Analysts 15-2051.01 Business Intelligence Analysts [] [] [] [] [] [] 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists 23.0 Information Technology and Computer Science 231010.0 Business Intelligence Analyst 23101012.0 Oracle Consultant / Analyst 2310.0 Business Intelligence 23101012.0 Oracle Consultant / Analyst 231010.0 Business Intelligence Analyst 2310.0 Business Intelligence 23.0 Information Technology and Computer Science 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists NaN NaN 56.0 Administrative and Support and Waste Managemen... 561.0 Administrative and Support Services 5613.0 Employment Services 56132.0 Temporary Help Services 561320.0 Temporary Help Services
2 85318b12b3331fa490d32ad014379df01855c557 9/6/2024 32:57.4 1.0 6/2/2024 7/7/2024 35.0 Data Analyst Taking care of people is at the heart of every... False 8.0 39063746.0 Sedgwick Sedgwick False [\n 2\n] [\n "Bachelor's degree"\n] 2.0 Bachelor's degree NaN NaN 1.0 Full-time (> 32 hours) 5.0 NaN False NaN 0.0 [None] NaN NaN NaN {\n "lat": 32.7766642,\n "lon": -96.7969879\n} RGFsbGFzLCBUWA== Dallas, TX 48113.0 Dallas, TX 19100.0 Dallas-Fort Worth-Arlington, TX 48.0 Texas 48113.0 Dallas, TX 48113.0 Dallas, TX 19100.0 Dallas-Fort Worth-Arlington, TX 19100.0 Dallas-Fort Worth-Arlington, TX 52.0 Finance and Insurance 524.0 Insurance Carriers and Related Activities 5242.0 Agencies, Brokerages, and Other Insurance Rela... 52429.0 Other Insurance Related Activities 524291.0 Claims Adjusting ET3037E0C947A02404 Data Analysts data analyst [\n "KS683TN76T77DQDVBZ1B"\n] [\n "Security Clearance"\n] 15-2051.01 Business Intelligence Analysts 15-2051.01 Business Intelligence Analysts [] [] [] [] [] [] 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists 23.0 Information Technology and Computer Science 231113.0 Data / Data Mining Analyst 23111310.0 Data Analyst 2311.0 Data Analysis and Mathematics 23111310.0 Data Analyst 231113.0 Data / Data Mining Analyst 2311.0 Data Analysis and Mathematics 23.0 Information Technology and Computer Science 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists NaN NaN 52.0 Finance and Insurance 524.0 Insurance Carriers and Related Activities 5242.0 Agencies, Brokerages, and Other Insurance Rela... 52429.0 Other Insurance Related Activities 524291.0 Claims Adjusting
3 1b5c3941e54a1889ef4f8ae55b401a550708a310 9/6/2024 32:57.4 1.0 6/2/2024 7/20/2024 48.0 Sr. Lead Data Mgmt. Analyst - SAS Product Owner About this role:\n\nWells Fargo is looking for... False 10.0 37615159.0 Wells Fargo Wells Fargo False [\n 99\n] [\n "No Education Listed"\n] 99.0 No Education Listed NaN NaN 1.0 Full-time (> 32 hours) 3.0 NaN False NaN 0.0 [None] NaN NaN NaN {\n "lat": 33.4483771,\n "lon": -112.0740373\n} UGhvZW5peCwgQVo= Phoenix, AZ 4013.0 Maricopa, AZ 38060.0 Phoenix-Mesa-Chandler, AZ 4.0 Arizona 4013.0 Maricopa, AZ 4013.0 Maricopa, AZ 38060.0 Phoenix-Mesa-Chandler, AZ 38060.0 Phoenix-Mesa-Chandler, AZ 52.0 Finance and Insurance 522.0 Credit Intermediation and Related Activities 5221.0 Depository Credit Intermediation 52211.0 Commercial Banking 522110.0 Commercial Banking ET2114E0404BA30075 Management Analysts sr lead data mgmt analyst sas product owner [] [] 15-2051.01 Business Intelligence Analysts 15-2051.01 Business Intelligence Analysts [] [] [] [] [] [] 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists 23.0 Information Technology and Computer Science 231113.0 Data / Data Mining Analyst 23111310.0 Data Analyst 2311.0 Data Analysis and Mathematics 23111310.0 Data Analyst 231113.0 Data / Data Mining Analyst 2311.0 Data Analysis and Mathematics 23.0 Information Technology and Computer Science 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists [\n 6\n] [\n "Data Privacy/Protection"\n] 52.0 Finance and Insurance 522.0 Credit Intermediation and Related Activities 5221.0 Depository Credit Intermediation 52211.0 Commercial Banking 522110.0 Commercial Banking
4 cb5ca25f02bdf25c13edfede7931508bfd9e858f 6/19/2024 00:00.0 0.0 6/2/2024 6/17/2024 15.0 Comisiones de $1000 - $3000 por semana... Comi... Comisiones de $1000 - $3000 por semana... Comi... False 15.0 0.0 Unclassified LH/GM False [\n 99\n] [\n "No Education Listed"\n] 99.0 No Education Listed NaN NaN 3.0 Part-time / full-time NaN NaN False 92500.0 0.0 [None] year 150000.0 35000.0 {\n "lat": 37.6392595,\n "lon": -120.9970014\n} TW9kZXN0bywgQ0E= Modesto, CA 6099.0 Stanislaus, CA 33700.0 Modesto, CA 6.0 California 6099.0 Stanislaus, CA 6099.0 Stanislaus, CA 33700.0 Modesto, CA 33700.0 Modesto, CA 99.0 Unclassified Industry 999.0 Unclassified Industry 9999.0 Unclassified Industry 99999.0 Unclassified Industry 999999.0 Unclassified Industry ET0000000000000000 Unclassified comisiones de por semana comiensa rapido [] [] 15-2051.01 Business Intelligence Analysts 15-2051.01 Business Intelligence Analysts [] [] [] [] [] [] 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists 23.0 Information Technology and Computer Science 231010.0 Business Intelligence Analyst 23101012.0 Oracle Consultant / Analyst 2310.0 Business Intelligence 23101012.0 Oracle Consultant / Analyst 231010.0 Business Intelligence Analyst 2310.0 Business Intelligence 23.0 Information Technology and Computer Science 15-0000 Computer and Mathematical Occupations 15-2000 Mathematical Science Occupations 15-2050 Data Scientists 15-2051 Data Scientists NaN NaN 99.0 Unclassified Industry 999.0 Unclassified Industry 9999.0 Unclassified Industry 99999.0 Unclassified Industry 999999.0 Unclassified Industry

Data Cleaning

selected_features = [
    'remote_type_name', 
    'state_name', 
    'city_name', 
    'lot_occupation_name',
    'min_edu_levels_name', 
    'salary'
]

X = df[selected_features]

X = X.dropna()
salary = X['salary']
X_others = X.drop(columns=['salary'])

X_encoded = pd.get_dummies(X_others)

scaler = StandardScaler()
salary_scaled = scaler.fit_transform(salary.values.reshape(-1, 1))

X_final = np.concatenate([X_encoded.values, salary_scaled], axis=1)

X_encoded.index = X.index

Train/Test Split

X_train, X_test, train_idx, test_idx = train_test_split(
    X_final, X_encoded.index, test_size=0.2, random_state=688
)

Finding K using Elbow Method

inertia = []
K_range = range(1, 11)

for k in K_range:
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(X_train)
    inertia.append(kmeans.inertia_)

plt.plot(K_range, inertia, marker='o')
plt.xlabel('Number of clusters (K)')
plt.ylabel('Inertia')
plt.title('Elbow Method For Optimal K')
plt.show()

Insights_ Elbow Method (K-Means Clustering)

In the plot above, we applied the Elbow Method to determine the optimal number of clusters (K) for our K-Means clustering model. The Y-axis represents the inertia, which is the sum of squared distances from each point to its assigned cluster center. The X-axis shows different values of K, ranging from 1 to 10.

We can observe that inertia decreases as K increases, which is expected — more clusters naturally lead to tighter groupings and lower within-cluster variance. However, the key insight from the elbow method is to look for the point where the rate of inertia reduction starts to slow down, forming an “elbow” shape. In our case, this elbow appears around K = 3 or 4, where the curve starts to flatten noticeably. Choosing a K beyond this point leads to only marginal improvements in inertia, suggesting diminishing returns.

Based on this analysis, we decided to use 4 clusters, which would likely balance interpretability and model effectiveness, helping us segment our data meaningfully without overfitting. This insight guides how we might group jobs, regions, or skills in our dataset for further analysis.

optimal_k = 4

Fit and Evaluate the Model

kmeans = KMeans(n_clusters=optimal_k, random_state=688)
train_clusters = kmeans.fit_predict(X_train)

true_labels_train = df.loc[train_idx, 'lot_occupation_name']

le = LabelEncoder()
true_labels_encoded = le.fit_transform(true_labels_train)

conf_mat = confusion_matrix(true_labels_encoded, train_clusters)

plt.figure(figsize=(12, 10))
sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted Cluster')
plt.ylabel('True ONET Label')
plt.title('Confusion Matrix: KMeans vs ONET Classification')
plt.show()

Insights_Confusion Matrix: KMeans vs. ONET Classification:

This confusion matrix compares the predicted clusters from our KMeans model with the true job role classifications based on ONET labels. Each row represents the actual ONET category, while each column shows how many jobs from that category were assigned to a given KMeans cluster. From the visualization, we can see that certain ONET labels aligned strongly with specific clusters—for instance, ONET label 1 is heavily concentrated in Cluster 0, with over 7,500 records, suggesting strong cohesion in that group. Similarly, ONET label 4 dominates Clusters 2 and 3, showing that KMeans successfully identified distinct subgroups within that label.

However, other labels such as label 3 appear more scattered, indicating overlap between job types or fuzzier cluster boundaries. Interestingly, Clusters 4 and 5 were unused, suggesting that although we specified six clusters, only four were meaningfully populated. This may imply either redundancy in our chosen K value or limited variation in the feature space.

Overall, this matrix provides a valuable check on how well our unsupervised clustering aligns with known job categories, revealing both strengths in classification and areas for improvement in feature design or model tuning.

Choropleth Map of Average Salary by U.S. State

import plotly.express as px
import us


salary_by_state = X.groupby("state_name")["salary"].mean().sort_values(ascending=False).reset_index()
salary_by_state['state_name'] = salary_by_state['state_name'].apply(
    lambda x: us.states.lookup(x).abbr if us.states.lookup(x) else x
)

fig = px.choropleth(
    salary_by_state,
    locations='state_name',
    locationmode='USA-states',
    color='salary',
    color_continuous_scale='Blues',
    scope='usa',
    labels={'avg_salary': 'Avg Salary'},
    title='Average Salary by U.S. State',
       width=1000,  
     height=600  
)

fig.update_layout(coloraxis_colorbar=dict(
    title="Avg Salary",
    tickprefix="$",
    ticks="outside"
))

fig.show()

Insights_Choropleth Map of Average Salary by U.S. State:

This choropleth map visualizes the average salary by state across the United States, with darker shades of blue indicating higher average compensation levels. From the visualization, we can observe that states such as California, New York, and Washington D.C. offer the highest average salaries, aligning with their large urban labor markets, dense concentrations of tech companies, and elevated costs of living.

In contrast, many southern and central states like Mississippi, New Mexico, and West Virginia report lower average salaries, reflecting a mix of lower living costs and possibly fewer high-paying industry hubs. As a student analyzing this data, this visualization emphasizes how geographic location significantly influences earning potential. It also highlights opportunities for remote work seekers to access high-paying markets without relocation, provided employers support flexible arrangements.

This map supports our broader analysis by revealing where economic opportunities are most concentrated, which can inform personal job strategies or workforce development planning.

Salary Variation Across KMeans Clusters

X_train_clustered = pd.DataFrame(X_train, index=train_idx)
X_train_clustered['Cluster'] = train_clusters
X_train_clustered['state_name'] = df.loc[train_idx, 'state_name']
X_train_clustered['remote_type_name'] = df.loc[train_idx, 'remote_type_name']
X_train_clustered['salary'] = df.loc[train_idx, 'salary']

X_train_clustered.head()
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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 -1.128943 3 Nevada Remote 67001.0
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
sns.boxplot(x='Cluster', y='salary', data=X_train_clustered)
plt.title('Salary Distribution by Cluster')
plt.xlabel('Cluster')
plt.ylabel('Salary')
plt.show()

Insights_Salary Variation Across KMeans Clusters

In this section, we analyzed how salaries vary across the job clusters produced by our KMeans model. The boxplot visualizes the distribution of salaries within each cluster (Cluster 0 to Cluster 3), allowing us to observe central tendencies, spread, and outliers. We can clearly see that Cluster 1 has the highest median salary, with a large number of high-earning outliers, suggesting it may represent higher-skilled or more specialized job roles. Clusters 0, 2, and 3 have comparatively lower median salaries, with Cluster 3 showing the lowest central value and the narrowest salary range. This suggests that Cluster 3 likely contains more entry-level or uniform-pay positions.

From our perspective, this analysis helps us understand the economic implications of unsupervised clustering. Even though we didn’t use salary as a feature to form the clusters, we see meaningful differences in pay across them. These differences validates that the clusters may capture latent job characteristics (e.g., seniority, technical skill, or industry) that correlate with compensation. It also suggests that further profiling of these clusters using features like remote_type_name or state_name could provide deeper insight into which job segments drive higher salaries.

State-Level Job Distribution Across Clusters

top_states = X_train_clustered['state_name'].value_counts().nlargest(10).index
subset = X_train_clustered[X_train_clustered['state_name'].isin(top_states)]

plt.figure(figsize=(12, 6))
sns.countplot(x='state_name', hue='Cluster', data=subset)
plt.title('Top 10 States Job Distribution by Cluster')
plt.xlabel('State')
plt.ylabel('Job Count')
plt.xticks(rotation=45)
plt.legend(title='Cluster')
plt.show()

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Insights_State-Level Job Distribution Across Clusters

This bar chart displays the job distribution across the top 10 U.S. states by KMeans cluster, helping us understand how different job types (as identified by clustering) are spread geographically. Each bar represents the number of job postings in a particular state, broken down by cluster label (0 through 3).

We observe that Texas dominates in job count across all clusters, especially Cluster 0, suggesting it is a highly diversified job market that spans multiple segments. North Carolina and Florida also show noticeable volumes, with Florida particularly rich in Cluster 2 jobs — which, based on earlier salary distribution analysis, may include mid- to lower-wage roles. California, while often associated with high-paying jobs, shows a more balanced distribution across Clusters 1, 2, and 3, implying a broader range of roles.

As we exploring geographic job trends, this visualization gives us insight into how job market segmentation varies by region. States like Texas and Florida seem to attract or offer a wide range of clustered job types, while states like New York or Washington have a more balanced but smaller presence across clusters. This supports the idea that location influences not just salary, but also the types of roles available, and could be a key factor for workforce planning or job-seeking strategies.

Job Seeker Recommendation:

Based on our salary distribution boxplot by cluster (see: Salary Distribution by Cluster), job seekers should focus on roles that fall into Cluster 1, which has the highest median salary and the widest spread of high-paying outliers. This suggests that Cluster 1 contains more senior or in-demand job types. To position themselves for these opportunities, candidates can review the common job titles and education levels within this cluster (as shown in the cluster composition plots by education and occupation) and tailor their resumes or training plans accordingly.

Additionally, the state-level cluster distribution bar chart (Top 10 States Job Distribution by Cluster) shows that states like Texas and Florida offer a wide range of job opportunities across all clusters. Job seekers may consider these locations for broader access to diverse roles, especially if they’re open to relocating or seeking hybrid positions.